2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2015)
Dec. 12, 2015 to Dec. 14, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2015.38
In the Big Data computing, improving performance with memory computing is one of hot spots. In the memory computing, the data deployment directly affects load balance and task efficiency. In the scene of memory computing of electric power data, two unsolved problems are: (1) only memory space, without the CPU frequency and nuclear number, could be considered for load balance and improving performance, (2) there are so many manual operations that it is difficult to complete data deployment automatically. This paper provides an electric power data deployment solution for distributed memory computing to solve the above challenges. In the solution, according to business logic and hardware configuration of cluster nodes, the data deployment strategy can be established. Then, the deployment scheme can be implemented with interface operation. Lastly, cluster nodes load data according to the deployment scheme. The solution has been applied to the Objectification Parallel Computing (OPC). The application result shows that OPC can achieve the best performance which can meet the demand of system efficiency and the operation of data deployment is simple.
Distributed databases, Business, Power systems, Big data, Hardware, Parallel processing
Z. Yang, C. Zhang, M. Hu and F. Lin, "An Electric Power Big Data Deployment Solution for Distributed Memory Computing," 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), Nanjing, China, 2015, pp. 155-161.